Controlling wellbore operations

ABSTRACT

Techniques for controlling a bottom hole assembly (BHA) to follow a planned wellbore path include determining sensor measurements from the BHA; determining a model of BHA dynamics based on the sensor measurements from the BHA; determining a weighting factor that corresponds to a drilling objective; determining an objective function comprising the drilling objective, weighted by the weighting factor, and one or more constraints; determining a control input to the BHA that satisfies the objective function and the one or more constraints; and applying the control input to the BHA.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a U.S. National Phase Application under 35 U.S.C.§371 and claims the benefit of priority to International ApplicationSerial No. PCT/US2013/073661, filed on Dec. 6, 2013, the contents ofwhich are hereby incorporated by reference.

TECHNICAL BACKGROUND

This disclosure relates to automated management of wellbore operationfor the production of hydrocarbons from subsurface formations.

BACKGROUND

Drilling for hydrocarbons, such as oil and gas, typically involves theoperation of drilling equipment at underground depths that can reachdown to thousands of feet below the surface. Such remote distances ofdownhole drilling equipment, combined with unpredictable downholeoperating conditions and vibrational drilling disturbances, createsnumerous challenges in accurately controlling the trajectory of awellbore. Compounding these problems is often the existence ofneighboring wellbores, sometimes within close proximity of each other,that restricts the tolerance for drilling error. Drilling operationstypically collect measurements from downhole sensors, located at or neara bottom hole assembly (BHA), to detect various conditions related tothe drilling, such as position and angle of the wellbore trajectory,characteristics of the rock formation, pressure, temperature, acoustics,radiation, etc. Such sensor measurement data is typically transmitted tothe surface, where human operators analyze the data to adjust thedownhole drilling equipment. However, sensor measurements can beinaccurate, delayed, or infrequent, limiting the effectiveness of usingsuch measurements. Often, a human operator is left to use best-guessestimates of the wellbore trajectory in controlling the drillingoperation.

DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example of an implementation of at least a portionof a wellbore system in the context of a downhole operation;

FIG. 2 illustrates an example of a processing flow for model-basedpredictive control that dynamically adapts weighting factors in responseto changing conditions in the wellbore;

FIG. 3 illustrates a 3-dimensional example of correlated uncertaintyvalues between different directions in a wellbore trajectory;

FIGS. 4A and 4B illustrate examples of determining an anti-collisiondirection for weighting factor adaptation;

FIG. 5 is a flow diagram of an example of a process of weight adaptationand synthesis;

FIG. 6 is a flow chart of an example process for performing model-basedpredictive control of a BHA;

FIG. 7 is a flow chart of an example of further details of determiningat least one weighting factor based on at least one of the model of BHAdynamics or the sensor measurements from the BHA;

FIG. 8 is a flow chart of an example of further details of determiningat least one weighting factor and determining an objective functionweighted by the at least one weighting factor;

FIG. 9 is a flow chart of an example of further details of determiningan objective function and determining a control input to the BHA thatsatisfies the objective function; and

FIG. 10 is a block diagram of an example of a control system on whichsome examples may operate.

DETAILED DESCRIPTION

This disclosure describes, generally, automated control of wellboredrilling operations by making model-based predictive control decisionsfor the BHA. In particular, techniques are described that dynamicallyadapt the BHA control inputs to emphasize different drilling objectivesat different times based on changing conditions in the wellbore. Thechanging conditions in the wellbore may be determined using any suitablesource of information, such as sensor measurements, model-basedpredictions, and/or wellbore planning information.

The BHA control inputs may be adapted to selectively emphasize (orde-emphasize) one or more objectives associated with the drillingoperation, in response to changing conditions in the wellbore. Asexamples, the objectives may relate to reducing deviation from a plannedwellbore path, reducing input energy consumption for the BHA, or anyother suitable objective related to the drilling operation. During adrilling operation, changing conditions in the wellbore (e.g., differentlayers of rock, differently-shaped portions of a planned wellbore path,etc.) may result in different objectives being more or less important atdifferent times to maintain an overall efficient and cost-effectivedrilling operation.

In some examples, the one or more objectives may be combined in a singleoverall objective function, in which different objectives are emphasizedby different amounts, using one or more weighting factors. The adaptivenature of the BHA control inputs may be implemented by adapting theweighting factors to selectively emphasize different objectives in anobjective function, and solving for the BHA control input that satisfiesthe overall objective function. The weighting factors may automaticallyadapt to changes in conditions in the wellbore. As examples, theweighting factors may automatically adapt to changes in the amount ofuncertainty in the wellbore trajectory, different angles and turns alongthe planned wellbore path, the existence of neighboring wellbores thatpose threats of collision, or other conditions in and around thewellbore that may be relevant to directional drilling systems.

In a general implementation, a computer-implemented method ofcontrolling a bottom hole assembly (BHA) to follow a planned wellborepath, the method includes determining sensor measurements from the BHA;determining a model of BHA dynamics based on the sensor measurementsfrom the BHA; determining a weighting factor that corresponds to adrilling objective; determining an objective function comprising thedrilling objective, weighted by the weighting factor, and one or moreconstraints; determining a control input to the BHA that satisfies theobjective function and the one or more constraints; and applying thecontrol input to the BHA.

Other general implementations include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.A system of one or more computers can be configured to performoperations to perform the actions. One or more computer programs can beconfigured to perform particular operations or actions by virtue ofincluding instructions that, when executed by data processing apparatus,cause the apparatus to perform the actions.

In a first aspect combinable with any of the general implementations,determining a weighting factor that corresponds to a drilling objectivefurther includes determining a weighting factor based on at least one ofthe model of BHA dynamics or the sensor measurements from the BHA.

In a second aspect combinable with any of the previous aspects,determining a weighting factor based on at least one of the model of BHAdynamics or the sensor measurements from the BHA includes determining atleast one of an uncertainty of a measured wellbore trajectory, a shapeof the wellbore, or collision avoidance information;

determining a weight based on at least one of the uncertainty of ameasured wellbore trajectory, the shape of the wellbore, or thecollision avoidance information; and synthesizing the weight into theweighting factor.

In a third aspect combinable with any of the previous aspects,determining an uncertainty of a measured wellbore trajectory includesdetermining covariance values between a plurality of azimuth values andinclination values for the wellbore trajectory.

In a fourth aspect combinable with any of the previous aspects,determining a weighting factor that corresponds to a drilling objectiveincludes tightening a constraint on the control input to the BHA in adirection in which the uncertainty of the measured wellbore trajectoryhas increased from a previous measurement time.

In a fifth aspect combinable with any of the previous aspects,tightening a constraint on the control input to the BHA includesdetermining an increased value of a weighting factor associated with thecontrol input to the BHA.

In a sixth aspect combinable with any of the previous aspects, thedrilling objective includes a predicted deviation from the plannedwellbore trajectory, and determining a weighting factor that correspondsto a drilling objective includes loosening a constraint on the predicteddeviation from the planned wellbore trajectory in a direction in whichthe uncertainty of the measured wellbore trajectory has increased from aprevious measurement time.

In a seventh aspect combinable with any of the previous aspects,loosening a constraint on the predicted deviation from the plannedwellbore path includes determining a reduced value of a weighting factorassociated with the predicted deviation from the planned wellbore path.

In an eighth aspect combinable with any of the previous aspects,determining a shape of the wellbore includes determining a radius ofcurvature for subsequent portion of the planned wellbore path.

In a ninth aspect combinable with any of the previous aspects, thedrilling objective includes a predicted deviation from the plannedwellbore trajectory, and determining a weighting factor includesreducing a constraint on the predicted deviation from the plannedwellbore trajectory in a direction in which the radius of curvature forthe future portion of the planned wellbore path has decreased from aprevious measurement time.

In a tenth aspect combinable with any of the previous aspects,determining collision avoidance information includes determining adirection in which a collision with another wellbore is most likely tooccur.

In an eleventh aspect combinable with any of the previous aspects, thedrilling objective includes a predicted deviation from the plannedwellbore trajectory, and determining a weighting factor includesincreasing a constraint on the predicted deviation from the plannedwellbore trajectory in the direction in which a collision with anotherwellbore is most likely to occur.

In a twelfth aspect combinable with any of the previous aspects,determining covariance values between a plurality of azimuth values andinclination values for the wellbore trajectory further includesdetermining a plurality of azimuth measurements and inclinationmeasurements received from sensors of the BHA; and determiningcovariance values between the plurality of azimuth measurements andinclination measurements received from the sensors of the BHA.

In thirteenth aspect combinable with any of the previous aspects,determining covariance values between a plurality of azimuth values andinclination values for the wellbore trajectory further includesdetermining a plurality of azimuth predictions and inclinationpredictions based on the model of BHA dynamics; and determiningcovariance values between the plurality of azimuth predictions andinclination predictions based on the model of BHA dynamics.

In a fourteenth aspect combinable with any of the previous aspects,determining an objective function includes determining a predictedfuture deviation from the planned wellbore path; determining a predictedfuture cost of applying the control input to the BHA; and determining aweighted combination, weighted by the weighting factor, of the predictedfuture deviation from the planned wellbore path and the predicted futurecost of applying the control input to the BHA.

In a fifteenth aspect combinable with any of the previous aspects,determining a weighting factor includes determining a first weightingfactor for the predicted future deviation from the planned wellborepath; and determining a second weighting factor for the predicted futurecost of applying the control input to the BHA.

In a sixteenth aspect combinable with any of the previous aspects,determining a control input to the BHA that satisfies the objectivefunction includes determining a control input to the BHA that minimizesthe weighted combination of the predicted future deviation from theplanned wellbore path and the predicted future cost of applying thecontrol input to the BHA over a subsequent period of time.

In a seventeenth aspect combinable with any of the previous aspects, thepredicted future cost of applying the control input to the BHA includesa predicted energy consumption for the BHA.

An eighteenth aspect combinable with any of the previous aspects furtherincludes determining a candidate control input to the BHA; determining apredicted wellbore trajectory, based on the candidate control input tothe BHA and the model of BHA dynamics; and determining a predictedfuture deviation from the planned wellbore path based on a deviationbetween the predicted wellbore trajectory and the planned wellbore path.

In a nineteenth aspect combinable with any of the previous aspects,determining a control input to the BHA includes determining at least oneof a first bend angle control, a second bend angle control, a firstpacker control, or a second packer control.

A twentieth aspect combinable with any of the previous aspects furtherincludes determining updated sensor measurements from the BHA;determining an updated model of BHA dynamics based on the updated sensormeasurements from the BHA; determining an updated weighting factor andan updated objective function based on at least one of the updated modelof BHA dynamics or the updated sensor measurements from the BHA; andautomatically adapting the control input to the BHA that satisfies theupdated objective function based on the updated weighting factor.

In a twenty-first aspect combinable with any of the previous aspects,determining covariance values between the plurality of azimuthmeasurements and inclination measurements further includes determining across-correlation between uncertainty values in two different directionsfrom the wellbore trajectory.

Various implementations of a control system for wellbore drillingaccording to the present disclosure may include none, one or some of thefollowing features. For example, the system may improve the stabilityand robustness of drilling operations. In particular, techniquesdescribed herein may enable more accurate and precise control of thewellbore trajectory despite varying and unpredictable conditions in thewellbore environment.

For example, if a certain portion of the wellbore yields larger errorand more uncertain measurements, then it may be desirable to put moreweight on the objective of reducing input energy, thus constraining theinput to employ more conservative drilling during times when themeasured trajectory may not be an accurate reflection of the truetrajectory in the wellbore. As another example, if a certain portion ofthe planned wellbore path has a sharp turn, then it may be desirable toput less weight on the objective of staying close to the plannedwellbore path during those times, to allow more leeway while making thesharp turn and to avoid throttling the input (e.g., if staying close tothe planned path is too costly or even impossible). By adaptivelyputting more or less emphasis on different objectives at different timesduring the drilling operation, techniques described herein may enablemore efficient and more accurate drilling operation despite changingconditions in the wellbore.

In some examples, a model of BHA dynamics may be used to generatepredictions of future wellbore trajectory, and the BHA control inputsmay be proactively adapted based on the predicted wellbore trajectory.The model of BHA dynamics may be updated as new measurements are takenand as new control inputs are received, to enable close tracking of thetrue wellbore trajectory, for example, to enable less prediction errorof the wellbore trajectory. The system may use these predictions, aswell as planned wellbore path information and/or other information, toanticipate future changes in the wellbore and proactively adapt thedrilling operation.

For example, the system may increase or decrease selected weightingfactors of one or more objectives based on the anticipated changes inthe wellbore, and automatically determine BHA control inputs thatsatisfy the adapted weighted objectives and one or more constraints.Satisfying the objectives may include, for example, performing anoptimization (e.g., minimizing a cost function, maximizing a utilityfunction, etc.), or may include finding sub-optimal solutions thatapproximate optimal solutions (e.g., numerical approximations thataccount for computational complexity, etc.), or may include satisfyingother suitable objectives related to the drilling process. Thedetermination of BHA control inputs that satisfy the objectives mayinvolve satisfying one or more constraints, for example, the maximumbending angle, the maximum power available, etc.

The model along with the constraints determines a profile of allpossible future BHA behaviors, based on which the optimal control inputsand the associated future BHA dynamics are determined by minimizing theobjective function.

The downhole environment around a BHA in a wellbore is generally acomplex system. In some examples, the system may include at least 4control variables and 12 measurements. Conventional control strategiesmay not easily apply to BHA systems for various reasons, including thefollowing. The interactions between different inputs and outputs can bestrong and unpredictable, e.g., inclination measurements may depend onmost of the control variables, such as two bend angles and packerinflation. In such scenarios, conventional design techniques, such asproportional-integral-derivative (PID), may be limited in achievingdesired performance. For example, if an optimal solution to an objectivefunction is desired, then PID controllers may be unable to achieve thedesired optimal performance. Another difficulty is that the number ofoutputs may be greater than the number of inputs, and it may not alwaysbe clear how to decouple the interactions between certain inputs andoutput. This may result in complex and numerous options that complicatethe design of the BHA input controls. In such scenarios, the performanceof the drilling operation typically depends on the tuning skills of acontrol system designer, which may be subject to human error. Anotherdifficulty with the number of measurements being greater than the numberof control variables is that, under many cases, it may be difficult forall of the measurements to track their planned target values withoutencountering some offset. Such offset may lead to uncertainty in how tocontrol the wellbore trajectory, which may lead to an overly-aggressivecontrol that results in different outputs competing with each other. Forexample, if a near inclination sensor requires a larger bend angle, thenthis may result in more errors and uncertainty in one of the farinclination sensors. In some scenarios, this may result in a reducedstability margin, rendering the drilling operation more difficult tocontrol accurately.

Techniques described herein provide a control strategy based onmodel-based predictive control (MPC), which enables regulating complexBHA systems, even those that may have strong interactions, whilesatisfying (e.g., optimizing) an overall objective function of thedrilling operation and any associated constraints. Moreover, inscenarios in which the surrounding environment and design specificationchange quickly during a directional drilling operation, an adaptiveweight tuning algorithm may be performed in conjunction with the MPCstrategy to achieve a more robust and precise control.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

FIG. 1 illustrates a portion of one implementation of a deviatedwellbore system 100 according to the present disclosure. Although shownas a deviated system (e.g., with a directional, horizontal, or radiussedwellbore), the system can include a relatively vertical wellbore only(e.g., including normal drilling variations) as well as other types ofwellbores (e.g., laterals, pattern wellbores, and otherwise). Moreover,although shown on a terranean surface, the system 100 may be located ina sub-sea or water-based environment. Generally, the deviated wellboresystem 100 accesses one or more subterranean formations, and provideseasier and more efficient production of hydrocarbons located in suchsubterranean formations. Further, the deviated wellbore system 100 mayallow for easier and more efficient fracturing or stimulationoperations. As illustrated in FIG. 1, the deviated wellbore system 100includes a drilling assembly 104 deployed on a terranean surface 102.The drilling assembly 104 may be used to form a vertical wellboreportion 108 extending from the terranean surface 102 and through one ormore geological formations in the Earth. One or more subterraneanformations, such as productive formation 126, are located under theterranean surface 102. As will be explained in more detail below, one ormore wellbore casings, such as a surface casing 112 and intermediatecasing 114, may be installed in at least a portion of the verticalwellbore portion 108.

In some implementations, the drilling assembly 104 may be deployed on abody of water rather than the terranean surface 102. For instance, insome implementations, the terranean surface 102 may be an ocean, gulf,sea, or any other body of water under which hydrocarbon-bearingformations may be found. In short, reference to the terranean surface102 includes both land and water surfaces and contemplates formingand/or developing one or more deviated wellbore systems 100 from eitheror both locations.

Generally, the drilling assembly 104 may be any appropriate assembly ordrilling rig used to form wellbores or wellbores in the Earth. Thedrilling assembly 104 may use traditional techniques to form suchwellbores, such as the vertical wellbore portion 108, or may usenontraditional or novel techniques. In some implementations, thedrilling assembly 104 may use rotary drilling equipment to form suchwellbores. Rotary drilling equipment is known and may consist of a drillstring 106 and a bottom hole assembly (BHA) 118. In someimplementations, the drilling assembly 104 may consist of a rotarydrilling rig. Rotating equipment on such a rotary drilling rig mayconsist of components that serve to rotate a drill bit, which in turnforms a wellbore, such as the vertical wellbore portion 108, deeper anddeeper into the ground. Rotating equipment consists of a number ofcomponents (not all shown here), which contribute to transferring powerfrom a prime mover to the drill bit itself. The prime mover suppliespower to a rotary table, or top direct drive system, which in turnsupplies rotational power to the drill string 106. The drill string 106is typically attached to the drill bit within the bottom hole assembly118. A swivel, which is attached to hoisting equipment, carries much, ifnot all of, the weight of the drill string 106, but may allow it torotate freely.

The drill string 106 typically consists of sections of heavy steel pipe,which are threaded so that they can interlock together. Below the drillpipe are one or more drill collars, which are heavier, thicker, andstronger than the drill pipe. The threaded drill collars help to addweight to the drill string 106 above the drill bit to ensure that thereis enough downward pressure on the drill bit to allow the bit to drillthrough the one or more geological formations. The number and nature ofthe drill collars on any particular rotary rig may be altered dependingon the downhole conditions experienced while drilling.

The drill bit is typically located within or attached to the bottom holeassembly 118, which is located at a downhole end of the drill string106. The drill bit is primarily responsible for making contact with thematerial (e.g., rock) within the one or more geological formations anddrilling through such material. According to the present disclosure, adrill bit type may be chosen depending on the type of geologicalformation encountered while drilling. For example, different geologicalformations encountered during drilling may require the use of differentdrill bits to achieve maximum drilling efficiency. Drill bits may bechanged because of such differences in the formations or because thedrill bits experience wear. Although such detail is not critical to thepresent disclosure, there are generally four types of drill bits, eachsuited for particular conditions. The four most common types of drillbits consist of: delayed or dragged bits, steel to rotary bits,polycrystalline diamond compact bits, and diamond bits. Regardless ofthe particular drill bits selected, continuous removal of the “cuttings”is essential to rotary drilling.

The circulating system of a rotary drilling operation, such as thedrilling assembly 104, may be an additional component of the drillingassembly 104. Generally, the circulating system has a number of mainobjectives, including cooling and lubricating the drill bit, removingthe cuttings from the drill bit and the wellbore, and coating the wallsof the wellbore with a mud type cake. The circulating system consists ofdrilling fluid, which is circulated down through the wellbore throughoutthe drilling process. Typically, the components of the circulatingsystem include drilling fluid pumps, compressors, related plumbingfixtures, and specialty injectors for the addition of additives to thedrilling fluid. In some implementations, such as, for example, during ahorizontal or directional drilling process, downhole motors may be usedin conjunction with or in the bottom hole assembly 118. Such a downholemotor may be a mud motor with a turbine arrangement, or a progressivecavity arrangement, such as a Moineau motor. These motors receive thedrilling fluid through the drill string 106 and rotate to drive thedrill bit or change directions in the drilling operation.

In many rotary drilling operations, the drilling fluid is pumped downthe drill string 106 and out through ports or jets in the drill bit. Thefluid then flows up toward the surface 102 within an annular space(e.g., an annulus) between the wellbore portion 108 and the drill string106, carrying cuttings in suspension to the surface. The drilling fluid,much like the drill bit, may be chosen depending on the type ofgeological conditions found under subterranean surface 102. For example,certain geological conditions found and some subterranean formations mayrequire that a liquid, such as water, be used as the drilling fluid. Insuch situations, in excess of 100,000 gallons of water may be requiredto complete a drilling operation. If water by itself is not suitable tocarry the drill cuttings out of the bore hole or is not of sufficientdensity to control the pressures in the well, clay additives (bentonite)or polymer-based additives, may be added to the water to form drillingfluid (e.g., drilling mud). As noted above, there may be concernsregarding the use of such additives in underground formations which maybe adjacent to or near subterranean formations holding fresh water.

In some implementations, the drilling assembly 104 and the bottom holeassembly 118 may operate with air or foam as the drilling fluid. Forinstance, in an air rotary drilling process, compressed air lifts thecuttings generated by the drill bit vertically upward through theannulus to the terranean surface 102. Large compressors may provide airthat is then forced down the drill string 106 and eventually escapesthrough the small ports or jets in the drill bit. Cuttings removed tothe terranean surface 102 are then collected.

As noted above, the choice of drilling fluid may depend on the type ofgeological formations encountered during the drilling operations.Further, this decision may be impacted by the type of drilling, such asvertical drilling, horizontal drilling, or directional drilling. In somecases, for example, certain geological formations may be more amenableto air drilling when drilled vertically as compared to drilleddirectionally or horizontally.

As illustrated in FIG. 1, the bottom hole assembly 118, including thedrill bit, drills or creates the vertical wellbore portion 108, whichextends from the terranean surface 102 towards the target subterraneanformation 124 and the productive formation 126. In some implementations,the target subterranean formation 124 may be a geological formationamenable to air drilling. In addition, in some implementations, theproductive formation 126 may be a geological formation that is lessamenable to air drilling processes. As illustrated in FIG. 1, theproductive formation 126 is directly adjacent to and under the targetformation 124. Alternatively, in some implementations, there may be oneor more intermediate subterranean formations (e.g., different rock ormineral formations) between the target subterranean formation 124 andthe productive formation 126.

In some implementations of the deviated wellbore system 100, thevertical wellbore portion 108 may be cased with one or more casings. Asillustrated, the vertical wellbore portion 108 includes a conductorcasing 110, which extends from the terranean surface 102 shortly intothe Earth. A portion of the vertical wellbore portion 108 enclosed bythe conductor casing 110 may be a large diameter wellbore. For instance,this portion of the vertical wellbore portion 108 may be a 17½″ wellborewith a 13⅜″ conductor casing 110. Additionally, in some implementations,the vertical wellbore portion 108 may be offset from vertical (e.g., aslant wellbore). Even further, in some implementations, the verticalwellbore portion 108 may be a stepped wellbore, such that a portion isdrilled vertically downward and then curved to a substantiallyhorizontal wellbore portion. The substantially horizontal wellboreportion may then be turned downward to a second substantially verticalportion, which is then turned to a second substantially horizontalwellbore portion. Additional substantially vertical and horizontalwellbore portions may be added according to, for example, the type ofterranean surface 102, the depth of one or more target subterraneanformations, the depth of one or more productive subterranean formations,and/or other criteria.

Downhole of the conductor casing 110 may be the surface casing 112. Thesurface casing 112 may enclose a slightly smaller wellbore and protectthe vertical wellbore portion 108 from intrusion of, for example,freshwater aquifers located near the terranean surface 102. The verticalwellbore portion 108 may than extend vertically downward toward akickoff point 120, which may be between 500 and 1,000 feet above thetarget subterranean formation 124. This portion of the vertical wellboreportion 108 may be enclosed by the intermediate casing 114. The diameterof the vertical wellbore portion 108 at any point within its length, aswell as the casing size of any of the aforementioned casings, may be anappropriate size depending on the drilling process.

Upon reaching the kickoff point 120, drilling tools such as logging andmeasurement equipment may be deployed into the wellbore portion 108. Atthat point, a determination of the exact location of the bottom holeassembly 118 may be made and transmitted to the terranean surface 102.Further, upon reaching the kickoff point 120, the bottom hole assembly118 may be changed or adjusted such that appropriate directionaldrilling tools may be inserted into the vertical wellbore portion 108.

As illustrated in FIG. 1, a curved wellbore portion 128 and a horizontalwellbore portion 130 have been formed within one or more geologicalformations. Typically, the curved wellbore portion 128 may be drilledstarting from the downhole end of the vertical wellbore portion 108 anddeviated from the vertical wellbore portion 108 toward a predeterminedazimuth gaining from between 9 and 18 degrees of angle per 100 feetdrilled. Alternatively, different predetermined azimuth may be used todrill the curved wellbore portion 128. In drilling the curved wellboreportion 128, the bottom hole assembly 118 often usesmeasurement-while-drilling (“MWD”) equipment to more precisely determinethe location of the drill bit within the one or more geologicalformations, such as the target subterranean formation 124. Generally,MWD equipment may be utilized to directionally steer the drill bit as itforms the curved wellbore portion 128, as well as the horizontalwellbore portion 130.

Alternatively to or in addition to MWD data being compiled duringdrilling of the wellbore portions shown in FIG. 1, certain high-fidelitymeasurements (e.g., surveys) may be taken during the drilling of thewellbore portions. For example, surveys may be taken periodically intime (e.g., at particular time durations of drilling, periodically inwellbore length (e.g., at particular distances drilled, such as every 30feet or otherwise), or as needed or desired (e.g., when there is aconcern about the path of the wellbore). Typically, during a survey, acompleted measurement of the inclination and azimuth of a location in awell (typically the total depth at the time of measurement) is made inorder to know, with reasonable accuracy, that a correct or particularwellbore path is being followed (e.g., according to a wellbore plan).Further, position may be helpful to know in case a relief well must bedrilled. High-fidelity measurements may include inclination fromvertical and the azimuth (or compass heading) of the wellbore if thedirection of the path is critical. These high-fidelity measurements maybe made at discrete points in the well, and the approximate path of thewellbore computed from the discrete points. The high-fidelitymeasurements may be made with any suitable high-fidelity sensor.Examples include, for instance, simple pendulum-like devices to complexelectronic accelerometers and gyroscopes. For example, in simplependulum measurements, the position of a freely hanging pendulumrelative to a measurement grid (attached to the housing of a measurementtool and assumed to represent the path of the wellbore) is captured onphotographic film. The film is developed and examined when the tool isremoved from the wellbore, either on wireline or the next time pipe istripped out of the hole.

The horizontal wellbore portion 130 may typically extend for hundreds,if not thousands, of feet within the target subterranean formation 124.Although FIG. 1 illustrates the horizontal wellbore portion 130 asexactly perpendicular to the vertical wellbore portion 108, it isunderstood that directionally drilled wellbores, such as the horizontalwellbore portion 130, have some variation in their paths. Thus, thehorizontal wellbore portion 130 may include a “zigzag” path yet remainin the target subterranean formation 124. Typically, the horizontalwellbore portion 130 is drilled to a predetermined end point 122, which,as noted above, may be up to thousands of feet from the kickoff point120. As noted above, in some implementations, the curved wellboreportion 128 and the horizontal wellbore portion 130 may be formedutilizing an air drilling process that uses air or foam as the drillingfluid.

The wellbore system 100 also includes a controller 132 that iscommunicative with the BHA 118. The controller 132 may be located at thewellsite (e.g., at or near drilling assembly 104) or may be remote fromthe wellsite. The controller 132 may also be communicative with othersystems, devices, databases, and networks. Generally, the controller 132may include a processor based computer or computers (e.g., desktop,laptop, server, mobile device, cell phone, or otherwise) that includesmemory (e.g., magnetic, optical, RAM/ROM, removable, remote or local), anetwork interface (e.g., software/hardware based interface), and one ormore input/output peripherals (e.g., display devices, keyboard, mouse,touchscreen, and others).

The controller 132 may at least partially control, manage, and executeoperations associated with the drilling operation of the BHA. In someaspects, the controller 132 may control and adjust one or more of theillustrated components of wellbore system 100 dynamically, such as, inreal-time during drilling operations at the wellbore system 100. Thereal-time control may be adjusted based on sensor measurement data orbased on changing predictions of the wellbore trajectory, even withoutany sensor measurements.

The controller 132 may perform such control operations based on a modelof BHA dynamics. The model of BHA dynamics may simulate various physicalphenomena in the drilling operation, such as vibrational disturbancesand sensor noise. The controller 132 may use the model of BHA dynamicsto determine a predicted wellbore trajectory and adapt one or moreweighting factors to selectively emphasize or de-emphasize differentobjectives related to the drilling.

In general, a model of BHA dynamics may rely on an underlying statevariable that evolves with time, representing changing conditions in thedrilling operation. The state variable in the model of BHA dynamics isan estimate of the true state of the BHA, from which estimates ofwellbore trajectory can be derived. The time evolution of the BHAdynamics may be represented by a discrete-time state-space model, anexample of which may be formulated as:

x(k+1)=Ax(k)+Bu(k)+w(k)

y(k)=Cx(k)+v(k)  (1)

where the matrices A, B, and C are system matrices that represent theunderlying dynamics of BHA drilling and measurement. The system matricesA, B, and C are determined by the underlying physics and mechanismsemployed in the drilling process. In practice, these matrices areestimated and modeled based on experience. The state x(t) is a vectorthat represents successive states of the BHA system, the input u(t) is avector that represents BHA control inputs, and the output y(t) is avector that represents the observed (measured) trajectory of wellbore.

In some aspects, the vector w(k) represents process noise and thevector, v(k), represents measurement noise. The process noise w accountsfor factors such as the effects of rock-bit interactions and vibrations,while the measurement noise v accounts for noise in the measurementsensors. The noise processes w(k) and v(k) may not be exactly known,although reasonable guesses can be made for these processes, and theseguesses can be modified based on experience. The noise vectors w(k) andv(k) are typically modeled by Gaussian processes, but non-Gaussian noisecan also be modeled by modifying the state x and matrix A to include notonly the dynamics described by the states variables, but also thedynamics of stochastic noise, as described further below.

In the examples discussed below, the BHA control input vector u(t)includes 6 control variables, representing first and second bend anglesof the BHA, a depth of the BHA, activation of first and second packers(e.g., by inflation of the packers, mechanical compression of thepackers, etc.), and a separation of the packers. The output vector y(t)includes 12 observed measurement values, including 6 measurement valuesfrom a near inclinometer and magnetometer package and another 6measurements from a far inclinometer and magnetometer package(hereinafter, “inc/mag”). The state vector x(t) is a vector of dimension12+n_(d), which includes 12 states that represent the actual azimuth andinclination values, as would be observed (measured) by the near and farinc/mag packages. The value n_(d) is the order of a disturbance modelwhich filters the un-modeled disturbances, and adds to the 12 statesrepresenting the system dynamics.

The state transition matrix A is therefore, in this example, a(12+n_(d)) by (12+n_(d)) dimensional state transition matrix thatrepresents the underlying physics, the matrix B is a (12+n_(d)) by 6dimensional matrix that governs the relation between the controlvariables and the state of the system, and the matrix C is a 12 by(12+n_(d)) matrix that governs the relation between the observations, y,and the state of the system, x. The matrices A, B, and C may bedetermined using any suitable estimation or modeling technique, such asa lumped-mass system model. There can be more states if more complexdynamic model is used to describe the system.

Due to the random noise and potential inaccuracies in modeling thesystem matrices A, B, and C, the state x of the model of BHA dynamics inEquation 1 is, in general, not exactly known, but rather inferred. Inthese scenarios, Equation 1 may be used to determine inferences, orestimates, of the state x and measurements y, rather than their truevalues. In particular, the model of Equation 1 may be used to generatepredictions of future values of state x and observations y. Suchpredictions may take into account actual measurements to refine themodel dynamics in Equation 1.

For example, the following equation may be used to obtain an estimate{circumflex over (x)} of the next state of the BHA system, in theabsence of any current measurements:

{circumflex over (x)}(k+1)=A{circumflex over (x)}(k)+Bu(k)

{circumflex over (y)}(k)=C{circumflex over (x)}(k)  (2)

If current measurements y are available, then predictions may begenerated by using Kalman filtering update equations:

{circumflex over (x)}(k+1)=A{circumflex over(x)}(k)+Bu(k)+K[y(k)−{circumflex over (y)}(k)]

{circumflex over (y)}(k)=C{circumflex over (x)}(k)  (3)

In Equation 3, y(k) represents the actual observation (e.g., provided byhigh-fidelity sensor measurements, MWD sensor measurements, or any othersuitable sensor measurements). The factor K (e.g., a time-varyingfactor), also known as the Kalman observation gain, represents acorrection factor to account for the error between the actual trajectoryand the estimated trajectory, y(k)−ŷ(k). In general, a larger value of Kimplies that more weight is given to the measured observation y(k) indetermining the estimate of the next state {circumflex over (x)}(k+1).Typically, K depends on the amount of vibration and reaction force thatis affecting the drill bit. The value of K may be chosen according toany suitable criterion (e.g., minimize mean-squared error of stateestimate, or any other suitable criterion), to achieve a desiredtradeoff between relative importance of measured observations andunderlying model dynamics.

The model of BHA dynamics in Equation 1 may be updated dynamically asnew information is received by the controller (e.g., the controller 132in FIG. 1). For example, matrices A and B may be affected by theoperating conditions in the wellbore, as the model (e.g., in equation 1)may be re-linearized as the operating condition changes. Suchre-linearization may be performed, for example, when it is determinedthat the BHA enters a different subsurface formation, or when thedrilling operation changes direction from a straight drilling directionto a curved drilling trajectory. In general, the model of BHA dynamicsmay be updated for a variety of reasons as the drilling environmentchanges.

A model-based predictive controller may use the model of BHA dynamics inEquation 1 to generate predictions of future wellbore trajectory, andbased on these predictions, determine BHA input controls that satisfy(e.g., optimize) a desired objective function while also satisfying oneor more constraints. The objective function may be a combination of oneor more objectives, weighted by at least one weighting factor. Theweighting factors may be dynamically adapted, based on measurements,predictions and other information, in response to changing conditions inthe wellbore.

As an illustrative example, the objective function may minimize, over afuture horizon of time, a weighted combination of two objectives: (1) adeviation from a planned wellbore path, and (2) an input energy consumedby the BHA, subject to a set of constraints. One example formulation ofsuch an example objective function is shown in Equations 4 and 5 below:

$\begin{matrix}{\min\limits_{u}{\sum_{k = t}^{n + T}\lbrack {{( {{y(k)} - {y^{sp}(k)}} )^{T}{Q(k)}( {{y(k)} - {y^{sp}(k)}} )} + {\Delta \; {u(k)}^{T}{S(k)}\Delta \; {u(k)}}} \rbrack}} & (4) \\{{subject}\mspace{14mu} {to}\mspace{14mu} \{ \begin{matrix}{{y(k)} = {{G(k)}{u(k)}}} \\{u^{\min} \leq {u(k)} \leq u^{\max}} \\{y^{\min} \leq {y(k)} \leq y^{\max}}\end{matrix} } & (5)\end{matrix}$

where y^(sp) is the planned wellbore path, t denotes the current timeinstant and T is the prediction horizon (which may be finite to obtain adynamic solution, or may be infinite to obtain a steady-state solution).The first term in the objective function in Equation 4 is a quadraticterm that corresponds to the objective of minimizing a squared deviationfrom the planned wellbore path, weighted by a weighting matrix Q(k)(which may be time-varying). The second term in Equation 5 is aquadratic term that corresponds to an objective of minimizing a squaredchange in the input controls, which represents input energy consumption,weighted by a weighting matrix S(k)(which may be time-varying). In thesecond term, it is assumed that the downhole power consumption isproportional to change rates of input controls (e.g., bend angles andactivation of packers). The change in input controls is the differencebetween the input controls in successive time steps, Δu(k)=u(k)−u(k−1).The function G(•) is an input-output representation based on the modelof BHA dynamics in Equation 1. In particular, the function G(•) may useeither Equation 2 (for updates without measurements) or Equation 3 (forupdates with measurements) to yield next-step predictions of themeasurement y based on a desired BHA input control u.

In the current time step t, after solving the objective function inEquation 4 to generate a desired control signal sequence u(k), k=t, t+1,. . . , (t+T), only the first control signal u(t) is applied to the BHA.At the next time instant t+1, the objective function in Equation 4 issolved again to generate the next sequence of controls, u(k), k=t+1, . .. , (t+1+T), of which the first control u(t+1) is applied to the BHA.These iterations continue, looking ahead T steps into the future toyield the best current-step control u that should be applied to the BHAto satisfy the objective function in Equation 4. In each iteration, theweighting factors in matrices Q and S may be updated based onmeasurements and predictions, to adapt to changing conditions in thewellbore.

In some examples, the weighting matrix Q may be in a diagonal form,where the terms along the diagonal distribute different weights to the12 inclinometer and magnetometer measurements. The resulting controleffort is determined by the mechanical and formation properties of therock layers in the drilling environment. For example, if Q is anidentity matrix, the weight given to each measurement variable is basedon their steady-state gains. However, in some examples, it may bedesirable to adjust the weighting factors in Q to selectively emphasize(or de-emphasize) specific measurements. A larger weighting factor for aparticular measurement variable indicates that the BHA input controlsshould be designed in a way that forces a tighter (more accurate)control for that particular measurement variable. Conversely, a smallerweighting factor for a particular measurement variable indicates thatthe BHA input controls can be designed in way that allows a looser (lessaccurate) control for that particular measurement variable.

In general, the objective function is not necessarily limited to anequation that expresses a weighting factor as a coefficient, as in theexample of Equation 4. More generally, the objective function mayrepresent any suitable combination of one or more objectives, and theweighting factor may represent any suitable quantification of a tradeoffbetween different objectives. For example, if the objective functionincludes the objectives of reducing deviation from a planned wellborepath and reducing input energy, then the weighting factor may generallyrepresent a tradeoff between deviation and energy.

Solving the objective function may involve any suitable technique, suchas iterative techniques, numerical techniques, or heuristic-basedtechniques (or other suitable techniques) in which a weighting factor isused to select a control input that achieves a desired tradeoff betweendifferent objectives. For example, if the objective function (withconstraints) is expressed as in Equations 4 and 5, then the solution mayinvolve any suitable optimization-solving technique. As another example,solving an objective function may involve a series of steps that lead toa desired control input. For example, a two-step process may include:first, a set of candidate BHA control inputs may be obtained thatachieves minimum or near-minimum input energy (for a given set ofconstraints), and second, an input may be chosen from the set ofcandidate inputs that achieves a desired deviation from the plannedwellbore path. The desired deviation may be chosen, with respect to theinput energy, using a suitable quantification of tradeoff (e.g., a ratiobetween energy and deviation, a maximum deviation for a given minimuminput energy, or some other notion of tradeoff), representing aweighting factor.

FIG. 2 illustrates an example of a processing flow for model-basedpredictive control of a BHA that dynamically adapts weighting factors inresponse to changing conditions in the wellbore. The example processingflow 200 of FIG. 2 may be performed, for example, by a controller (e.g.,controller 132 in FIG. 1) of a BHA (e.g., BHA 118 in FIG. 1). In theexample of FIG. 2, in block 202, an objective function (e.g., theobjective function in Equation 4) is solved in order to generate acontrol input 204 to a BHA 206 (e.g., BHA 118 in FIG. 1). The objectivefunction may be based on a model of BHA dynamics 208 (e.g., the model inEquation 1) and may include any number of suitable objectives, weightedby one or more weighting factors 210 (e.g., the weighting factors inmatrices Q and S in Equation 4).

In the example of FIG. 2, sensor measurements 212 from the BHA may beused to update the objective function solution in block 202, viameasurement feedback 214. In addition, one or more other parts of thedrilling operation may be updated based on sensor measurements 212. Forexample, the weighting factors in the weight matrices Q and S may bedynamically adapted, in block 216, based on changing conditions in thewellbore, using measurements 212 from the BHA. Furthermore, the model ofBHA dynamics may also be updated, in block 218, based on sensormeasurements 212. These dynamic updates may enable the BHA inputcontrols 204 to adapt to complicated changes in the downholeenvironment. Such adaptations may enable more accurate BHA controlinputs and more efficient overall drilling operations, as compared tousing constant weighting matrices that are pre-determined during adesign stage.

In some examples, the weight adaptation block 216 may use sensormeasurements 212 to determine an uncertainty of the wellbore trajectory,and may then adapt the weighting factors (e.g., the weighting factors inmatrices Q and S in Equation 4) based on the determined uncertainty. Insome examples, the weight adaptation block 216 may additionally oralternatively determine predictions of future uncertainty, using a modelof BHA dynamics and state update equations (e.g., state update Equations2 and/or 3). The weight adaptation block 216 may adapt the weightingfactors to put more or less emphasis on certain BHA input controls,based on the uncertainty of the wellbore trajectory in particulardirections.

As an example, vibrations in the drilling process may occur in differentdirections as the drilling operates in different portions of thewellbore. Such vibrations may increase the uncertainty of the wellboretrajectory. It may be desirable to dynamically increase weightingfactors in particular directions as the uncertainty grows (ordynamically decrease the weighting factors as uncertainty reduces). Theweight adaptation block 216 may automatically determine the uncertainty,based on measurements or model-based predictions, and adapt theweighting factors accordingly.

In addition, in the example of FIG. 2, the weight adaptation block 216,as well as the model update block 218 and the objective function solverin block 202, may also adapt to other information, such as wellboreplanning information 220. The planning information 220 may include, asexamples, a planned wellbore path and information regarding otherwellbores in the vicinity of the wellbore. In some examples, the weightadaptation block 216 and/or the model update block 218 may also utilizeplanning information 220 to update the weights (e.g., weighting factorsin matrices Q and S in Equation 4) and the model of BHA dynamics (e.g.,the model of BHA dynamics in Equation 1).

The weight adaptation block 216 may update the weighting factors inmatrices Q and S based on a synthesis of one or more weight adaptationmechanisms based on measurements, predictions, and/or planninginformation described above. These weighting factor updates may occur onany suitable time scale, such as every time step, or every time ameasurement is taken, as appropriate.

Some examples of weight adaption mechanisms are provided below, butother adaption mechanisms may also be used which are relevant todetermining the weighting factors (and, as a consequence, the BHAcontrol inputs). For example, the weight matrices Q and/or S may beadapted based on a measured characteristic of the downhole drilling,such as torque on the BHA, or based on a constraint on an input to theBHA, such as fluid flow, or angular position of downhole tools. Ingeneral, for any suitable input or output of the drilling operation, oneor more weighting factors may be defined to adaptively regulate aconstraint to be placed on that input or output.

The examples below describe three possible adaption mechanisms, usingthree different types of information, for dynamically adjusting theweighting factors. These adaptation mechanisms are based on: uncertaintyof the wellbore trajectory, planned wellbore path, and anti-collisioninformation.

As a first example of a weight adaptation mechanism, the weightingfactors may be determined based on uncertainty. In general, sensormeasurements (e.g., MWD data, survey data, etc.) may improve theaccuracy of wellbore tracking and trajectory estimation. In practice,however, the presence of sensor noise and process noise (e.g., reactionforce from rock formations, vibrations, etc.) create uncertainties inthe sensor measurements. The uncertainty of the measured wellboretrajectory can be characterized by a covariance matrix Σ_(y). In someexamples, the matrix Σ_(y) may be determined by computing covariancevalues between a plurality of measured azimuth values and measuredinclination values collected over time for the wellbore trajectory. Insome examples, in addition or as an alternative to using sensormeasurements, the matrix Σ_(y) may be determined by using predictions ofthe wellbore trajectory (e.g., as determined by the model of BHAdynamics state updates in Equations 2 and/or 3) to generate predictionsof future uncertainty in the wellbore trajectory.

In this example, the diagonal elements of the covariance matrix Σ_(y)are the variances for each of the 12 inc/mag measurements. Theoff-diagonal elements of the covariance matrix Σ_(y) are the covariancevalues between pairs of different inc/mag measurements, which describethe amount of correlations between the measurements.

In the example above, if a sensor measurement y has a large amount ofuncertainty in a particular direction (e.g., due to large vibrationalforces emanating from that drilling direction), then this usuallyindicates that the measurement is less trustworthy and the true wellboretrajectory has a wide margin of error in that direction. In suchscenarios, it may be desirable to apply more input control effort in thedirection of uncertainty. Additionally or alternatively, it may bedesirable to place less emphasis on maintaining a small deviationbetween the measured trajectory and the planned wellbore path in thedirection of uncertainty (since the measured trajectory is lesstrustworthy in that direction). In terms of weighting factors, this maybe implemented by either decreasing the weighting factor for the output(e.g., the weight matrix Q in the first quadratic term of Equation 4) orby increasing the weighting factor for the input (e.g., the weightmatrix S in the second quadratic term of Equation 4). In some examples,increasing the weighting factor for the input may correspond totightening a constraint on the input (e.g., reducing a maximum amplitudeconstraint, reducing an average power constraint, etc.). Analogously, insome examples, decreasing the weighting factor for the output maycorrespond to loosening a constraint on the output (e.g., increasing amaximum bound on deviation from the planned wellbore trajectory,increasing a probability of deviating beyond a predetermined amount fromthe planned wellbore trajectory, etc.).

For example, one way of quantifying the adaptation of output weightingfactor, Q, to account for uncertainty is:

Q=Q ₀Σ_(y) ⁻¹  (6)

where Q₀ is the relative importance of each output (measurement). Insome examples, Q₀ may be set as identity matrix 112, though Q₀ may beany suitable matrix that assigns weighting factors to differentmeasurement directions. The matrix formulation of Equation 6 enablesapplication of input controls along any cross-direction, withoutnecessarily being limited to any particular axis of direction.

FIG. 3 illustrates a 3-dimensional example of correlated uncertaintybetween directions in the wellbore trajectory. In this example, ahorizontal wellbore 300 has a trajectory along a drilling direction 302that is parallel to a first axis direction 304. It is determined, basedon sensor measurements, that the second axis direction 306 and the thirdaxis direction 308 are both equally affected by drilling vibrations. Asan example, the output uncertainty may be expressed as the covariancematrix

$\Sigma_{y} = {\begin{bmatrix}1 & 2 \\2 & 6\end{bmatrix}.}$

In this example, the diagonal values (1 and 6) are the variances of the2^(nd) and 3^(rd) axes. The off-diagonal values (2 and 2) are thecovariance (correlation) between the 2^(nd) and 3^(rd) axes. Since thecorrelation between the 2^(nd) and 3^(rd) axes is the same as thecorrelation between the 3^(rd) and 2^(nd) axes, the off-diagonalelements are the same

An illustration of this is shown in FIG. 3, in which the elliptical ring310 represents the uniform uncertainty between the second axis direction306 and the third axis direction 308, and the directional correlation312 represents the correlation between the second axis direction 306 andthe third axis direction 308. By Equation 6, the corresponding outputweight matrix is determined to be

${Q = \begin{bmatrix}3 & {- 1} \\{- 1} & 0.5\end{bmatrix}},$

using an initial weight matrix of Q₀=I. The off-diagonal elementssuggest the MPC should not only control the BHA along the second axisdirection 306 and the third axis direction 308 during drilling, butshould also take into account the cross-correlation between the secondand third axis directions (or, in general, between any two principalaxes), which is represented by the directional correlation 312 in FIG.3. This example illustrates how weight adaptation based on covariancematrix computations enables adapting the BHA input controls totrajectory uncertainties along any direction. Such adaptations mayreduce correlations in uncertainty and help to prevent vibrations alongone direction from affecting other directions of drilling.

Additionally or alternatively to adapting the output weight matrix Q,the input weight matrix S may be adapted to changes in uncertainty.Adapting the input weight matrix S may suppress or amplify the BHA inputcontrol movement. For example, when the uncertainty of the wellboretrajectory is large in a particular direction, it may be desirable toemploy more BHA control efforts in that particular direction (to improvethe accuracy of controlling the BHA in that direction), and the weightassociated with the particular input direction may be adapted toautomatically implement those changes. As an example, the followingequation may be used to determine the input movement weight matrix S:

S=GΣ ^(†)Σ_(y)  (7)

where G denotes the steady-state gain of the model (e.g., thesteady-state value of the input-output gain G(•) in Equation 5) and theoperation (•)^(\) denotes a pseudo-inverse (since the transfer functionmatrix of a drilling process may be non-square, i.e., the number ofcontrol variables and output measurements may not be equal). Equation 7translates output wellbore uncertainty into the BHA input controlefforts, according to the contribution of each input variable to theoverall output. As an illustrative example, assume that a nearinclination sensor in the third axis direction is controlled by two bendangles, and that the steady-state gain is G=[1 2]. The pseudo inverse isG ^(†)=[0.2 0.4]^(T), meaning that a unit uncertainty of inclinationmeasurement in the third axis direction results in input movement weight0.2 and 0.4 for the first and second bend angle, respectively. Thisresult coincides with intuition because the impact of the second bendangle is double the impact of the first bend angle, as suggested by thesteady-state gain.

Another factor to be considered in determining weighting factors, inaddition or as an alternative to using uncertainty of wellboretrajectory, is the shape of the wellbore. In some examples, the wellborepath is determined at a design stage or is provided by an upper leveldecision-making algorithm. In such scenarios, it may be possible to usea planned wellbore path as feedforward information to adjust the outputweight matrix Q. When the planned wellbore path has a sharp turn, i.e.,the radius of curvature becomes smaller, stochastic impacts in thedrilling, such as side forces, may become stronger, leading to a largeruncertainty matrix. In some examples, instead of waiting for the drillbit to become deflected and start turning due to the side forces (andconsequently the uncertainty becoming larger), a feedforward mechanismmay be used to proactively adapt the output weight matrix Q ahead oftime based on the planned wellbore path information. The feedforwardmechanism may be either model-based or may be data-driven. An example ofa model-based feedforward algorithm is represented by the output weightmatrix:

Q=diag{R ₁ ,R ₂ ,R ₃}  (8)

where R₁, R₂, and R₃ represent the radius of curvature with respect tothe first axis direction, the second axis direction, and the third axisdirection, respectively. In some examples, the weight matrix Q maycorrespond to one set of inc/mag measurement only. For all 12 inc/magmeasurements, four of the Q matrices may be stacked diagonally in asingle larger matrix. Alternatively, the feedforward algorithm inEquation 8 may be driven by data. For example, historical sensormeasurement data of a past portion (e.g., a few hundred feet) ofdrilling may be used to model the relationship between radius ofcurvature and an uncertainty matrix.

Another factor that may be considered in the weight adaptation algorithmis anti-collision safety information. When a wellbore is proximate toother existing wellbores, there may be a risk of drilling into the otherwellbores and causing a collision. In such scenarios, it is oftendesirable to utilize a tighter control along the direction in which acollision is most likely to occur.

FIGS. 4A and 4B illustrate examples of determining an anti-collisiondirection for weighting factor adaptation. In FIG. 4A, the wellbore 400has a trajectory along the drilling direction 402 corresponding to thefirst axis direction. A nearby wellbore 404 exists in a directionrelative to the wellbore 400 indicated by directional indicator 406. Inthis example, the collision avoidance direction 406 is not completelyalong one of the principal axes. This is illustrated in FIG. 4B, whichshows an example of a two-dimensional cross-sectional view of acollision-avoidance scenario. In FIG. 4B, the collision avoidancedirection between wellbores 408 and 410, indicated by directionalindicator 412, does not exist along either the second axis direction 414nor the third axis direction 416. In such scenarios, the weight matrix Qmay have off-diagonal elements that are non-zero.

The weight matrix may be designed in any suitable manner to reflect thecollision avoidance information. As an illustrative example, thetwo-dimensional example in FIG. 4B may have a corresponding weightmatrix Q expressed as:

$\begin{matrix}{Q = {{\begin{bmatrix}{\cos \mspace{14mu} \theta} & {{- \sin}\mspace{14mu} \theta} \\{\sin \mspace{14mu} \theta} & {\cos \mspace{14mu} \theta}\end{bmatrix}\begin{bmatrix}W & 0 \\0 & 1\end{bmatrix}}\begin{bmatrix}{\cos \mspace{14mu} \theta} & {\sin \mspace{14mu} \theta} \\{{- \sin}\mspace{14mu} \theta} & {\cos \mspace{14mu} \theta}\end{bmatrix}}} & (9)\end{matrix}$

where W is the weight along collision-avoidance direction 406 relativeto the other (orthogonal) directions. The angle θ in the example ofEquation 9 is the collision avoidance angle (e.g., in polarcoordinates). The example in FIG. 4B is a simplified example forillustrative purposes, and the general principle can be extended tothree dimensional space, for example, if there is another wellbore aheadin the drilling direction 402.

A weight synthesizer may be used to combine the three factors describedabove. In particular, weighting matrices Q determined in Equations 6, 8,and 9 may be synthesized into a single output weight matrix Q that maybe applied to an objective function (e.g., the objective function inEquation 4). Any suitable synthesis technique may be used to combine thedifferent matrices in Equations 6, 8, and 9 (and/or other matricesdetermined by other adaptation techniques), such as by taking a weightedcombination. Similarly, the matrix S determined in Equation 7 may becombined with other weight matrices determined by other adaptationtechniques. Other adaptation techniques may depend on any suitablemeasurement, prediction, or planning information relevant to thedrilling operations.

FIG. 5 illustrates a flow diagram of a process of weight adaptation andsynthesis (e.g., in weight adaptation module 216 in FIG. 2). In theexample of FIG. 5, the weight adaption process 500 uses both afeedforward function 502 and a feedback function 504. The feedforwardfunction 502 may use any suitable wellbore planning information (e.g.,planning information 220 in FIG. 2), examples of which include a plannedwellbore path 506 and anti-collision information 508. The feedbackfunction 504 may use any suitable feedback information 510 that isgenerated as a result of the drilling operation.

The feedback information 510 may include, for example, an uncertainty ofthe wellbore trajectory as generated by an uncertainty model 512. Theuncertainty model 512 may determine the uncertainty of the wellboretrajectory based on drilling information 514, which may includemeasurements and/or control variables (e.g., sensor measurements 212and/or control variables 204 in FIG. 2). For example, the uncertaintymodel 512 may determine a covariance matrix (e.g., the covariance matrixΣ_(y) in Equations 6 and 7) by either computing covariance valuesbetween sensor measurements, or by generating predictions of uncertaintyusing a model of BHA dynamics.

The feedback function 504 uses the uncertainty measurements and/orpredictions to generate feedback weight information 516. The feedbackweight information 516 is synthesized, along with feedforward weightinformation 518 by the feedforward function 502, by the synthesizingoperation 520 to generate weighting factors 522 (e.g., weight matrices Qand S in Equation 4). In some examples, the weight synthesizing process500 outputs the weighting factors 522 over a future prediction horizon(e.g., the finite horizon T in Equation 4 or an infinite horizon for asteady-state solution). The weighting factors 522 are then input to themodel-based predictive control 524 (e.g., as implemented by objectivefunction solver in block 202 of FIG. 2) to generate BHA input controls.

After obtaining input/output data drilling information 514 that resultsfrom the drilling operation, the uncertainty of the wellbore trajectoryis updated by uncertainty model 512, which is sent back to the feedbackfunction 504 for updates to the feedback weight information 516.Meanwhile, the planned wellbore path may be updated and input to thefeedforward function 502, which recalculates the feedforward weightinformation 518 based on the updated planning information.

FIG. 6 is a flow chart of an example process 600 for performingmodel-based predictive control of a BHA. One or more steps of theexample process of FIG. 6 may be performed by a wellbore controller(e.g., controller 132 in FIG. 1). In this example, the controllerdetermines sensor measurements from the BHA (602). The controller thendetermines a model of BHA dynamics (e.g., the model in Equation 1) basedon the sensor measurements from the BHA (604). The controller thendetermines a weighting factor (e.g., weighting factors in matrices Q andS in Equation 4) that corresponds to a drilling objective (606). Thecontroller determines an objective function (e.g., the objectivefunction in Equation 4) that includes the drilling objective, weightedby the weighting factor, and one or more constraints (e.g., theconstraints in Equation 5) (608). The controller then determines acontrol input to the BHA that satisfies the objective function (e.g.,optimizes the objective function in Equation 4) and the one or moreconstraints (610), and applies the control input to the BHA (612).

FIG. 7 is a flow chart of an example of further details of determining aweighting factor (e.g., weighting factors in matrices Q and S inEquation 4) that corresponds to a drilling objective (e.g., step 606 inFIG. 6). In this example, the controller determines at least one of: anuncertainty of a measured wellbore trajectory, a shape of the wellbore,or collision avoidance information (700). The controller then determinesa weight (e.g., the weights in weighting matrices Q and/or S inEquations 6 to 9) based on at least one of the uncertainty of a measuredwellbore trajectory, the shape of the wellbore, or the collisionavoidance information (702). The controller then synthesizes the weightinto the weighting factor (e.g., weighting matrices determined inEquations 6, 8, and 9 may be synthesized into a single output weightmatrix Q that may be applied to the objective function in Equation 4)(704).

FIG. 8 is a flow chart of an example of further details of determiningan objective function that includes the drilling objective, weighted bythe weighting factor, and one or more constraints (e.g., step 608 inFIG. 6). In this example, the controller determines a predicted futuredeviation from the planned wellbore path (800). The controller alsodetermines a predicted future cost of applying the control input to theBHA (802). The controller then determines a weighted combination,weighted by the weighting factor, of the predicted future deviation fromthe planned wellbore path and the predicted future cost of applying thecontrol input to the BHA (804).

FIG. 9 is a flow chart of an example of further details of determiningan objective function (e.g., the objective function in Equation 4) thatincludes the drilling objective, weighted by the weighting factor, andone or more constraints (e.g., step 608 in FIG. 6) and determining acontrol input to the BHA that satisfies the objective function and theone or more constraints (e.g., step 610 in FIG. 6). In this example, thecontroller determines a weighted combination of the predicted futuredeviation from the planned wellbore path and a predicted future cost ofapplying the control input to the BHA (e.g., step 804 in FIG. 8). Thecontroller then determines a control input to the BHA that minimizes(e.g., as in Equation 4) the weighted combination of the predictedfuture deviation from the planned wellbore path and the predicted futurecost of applying the control input to the BHA over a subsequent periodof time (900).

FIG. 10 is a block diagram of an example of a computer system 1000. Forexample, referring to FIG. 1, one or more parts of the controller 132could be an example of the system 1000 described here, such as acomputer system used by any of the users who access resources of thewellbore system 100. The system 1000 includes a processor 1010, a memory1020, a storage device 1030, and an input/output device 1040. Each ofthe components 1010, 1020, 1030, and 1040 can be interconnected, forexample, using a system bus 1050. The processor 1010 is capable ofprocessing instructions for execution within the system 1000. In someimplementations, the processor 1010 is a single-threaded processor. Insome implementations, the processor 1010 is a multi-threaded processor.In some implementations, the processor 1010 is a quantum computer. Theprocessor 1010 is capable of processing instructions stored in thememory 1020 or on the storage device 1030. The processor 1010 mayexecute operations such as determining a model of BHA dynamics,determining a weighting factor, determining a control input to the BHA,etc. (e.g., FIGS. 6 to 9).

The memory 1020 stores information within the system 1000. In someimplementations, the memory 1020 is a computer-readable medium. In someimplementations, the memory 1020 is a volatile memory unit. In someimplementations, the memory 1020 is a non-volatile memory unit.

The storage device 1030 is capable of providing mass storage for thesystem 1000. In some implementations, the storage device 1030 is acomputer-readable medium. In various different implementations, thestorage device 1030 can include, for example, a hard disk device, anoptical disk device, a solid-date drive, a flash drive, magnetic tape,or some other large capacity storage device. In some implementations,the storage device 1030 may be a cloud storage device, e.g., a logicalstorage device including multiple physical storage devices distributedon a network and accessed using a network. In some examples, the storagedevice may store long-term data, such as rock formation data or ROPdesign capabilities. The input/output device 1040 provides input/outputoperations for the system 1000. In some implementations, theinput/output device 1040 can include one or more of a network interfacedevices, e.g., an Ethernet card, a serial communication device, e.g., anRS-232 port, and/or a wireless interface device, e.g., an 802.11 card, a3G wireless modem, a 4G wireless modem, or a carrier pigeon interface. Anetwork interface device allows the system 1000 to communicate, forexample, transmit and receive instructions to and from the controller132 in FIG. 1. In some implementations, the input/output device caninclude driver devices configured to receive input data and send outputdata to other input/output devices, e.g., keyboard, printer and displaydevices 1060. In some implementations, mobile computing devices, mobilecommunication devices, and other devices can be used.

A server (e.g., a server forming a portion of the controller 132 or thewellbore system 100 shown in FIG. 1) can be realized by instructionsthat upon execution cause one or more processing devices to carry outthe processes and functions described above, for example, such asdetermining weighting factors, determining a control input to the BHAthat satisfies an objective function, etc. (e.g., FIGS. 6 to 9). Suchinstructions can include, for example, interpreted instructions such asscript instructions, or executable code, or other instructions stored ina computer readable medium. Different components of a wellbore system100 can be distributively implemented over a network, such as a serverfarm, or a set of widely distributed servers or can be implemented in asingle virtual device that includes multiple distributed devices thatoperate in coordination with one another. For example, one of thedevices can control the other devices, or the devices may operate undera set of coordinated rules or protocols, or the devices may becoordinated in another fashion. The coordinated operation of themultiple distributed devices presents the appearance of operating as asingle device.

The features described can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The apparatus can be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device, for execution by a programmableprocessor; and method steps can be performed by a programmable processorexecuting a program of instructions to perform functions of thedescribed implementations by operating on input data and generatingoutput. The described features can be implemented advantageously in oneor more computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that can be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program can be written in anyform of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both.Elements of a computer can include a processor for executinginstructions and one or more memories for storing instructions and data.Generally, a computer can also include, or be operatively coupled tocommunicate with, one or more mass storage devices for storing datafiles; such devices include magnetic disks, such as internal hard disksand removable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,ASICs (application-specific integrated circuits).

To provide for interaction with a user, the features can be implementedon a computer having a display device such as a CRT (cathode ray tube)or LCD (liquid crystal display) monitor for displaying information tothe user and a keyboard and a pointing device such as a mouse or atrackball by which the user can provide input to the computer.

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include, e.g., a LAN, a WAN, and thecomputers and networks forming the Internet.

The computer system can include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork, such as the described one. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

In addition, the logic flows depicted in the figures do not require theparticular order shown, or sequential order, to achieve desirableresults. In addition, other steps may be provided, or steps may beeliminated, from the described flows, and other components may be addedto, or removed from, the described systems. Accordingly, otherimplementations are within the scope of the following claims.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made. For example,additional aspects of processes 600 may include more steps or fewersteps than those illustrated in FIGS. 6 to 9. Further, the stepsillustrated in FIGS. 6 to 9 may be performed in different successionsthan that shown in the figures. Moreover, although the concepts havebeen described in the context of a wellbore drilling system, theconcepts could be applied to other processes as well. For example, inconnection with medical endoscopic examination or other applicationswhere an instrument is inserted and controlled inside of an unknownenvironment. Accordingly, other implementations are within the scope ofthe following claims.

What is claimed is:
 1. A computer-implemented method of controlling abottom hole assembly (BHA) to follow a planned wellbore path, the methodcomprising: determining sensor measurements from the BHA; determining amodel of BHA dynamics based on the sensor measurements from the BHA;determining a weighting factor that corresponds to a drilling objective;determining an objective function comprising the drilling objective,weighted by the weighting factor, and one or more constraints;determining a control input to the BHA that satisfies the objectivefunction and the one or more constraints; and applying the control inputto the BHA.
 2. The computer-implemented method of claim 1, whereindetermining a weighting factor that corresponds to a drilling objectivefurther comprises: determining a weighting factor based on at least oneof the model of BHA dynamics or the sensor measurements from the BHA. 3.The computer-implemented method of claim 2, wherein determining aweighting factor based on at least one of the model of BHA dynamics orthe sensor measurements from the BHA comprises: determining at least oneof an uncertainty of a measured wellbore trajectory, a shape of thewellbore, or collision avoidance information; determining a weight basedon at least one of the uncertainty of a measured wellbore trajectory,the shape of the wellbore, or the collision avoidance information; andsynthesizing the weight into the weighting factor.
 4. Thecomputer-implemented method of claim 3, wherein determining anuncertainty of a measured wellbore trajectory comprises determiningcovariance values between a plurality of azimuth values and inclinationvalues for the wellbore trajectory.
 5. The computer-implemented methodof claim 4, wherein determining a weighting factor that corresponds to adrilling objective comprises tightening a constraint on the controlinput to the BHA in a direction in which the uncertainty of the measuredwellbore trajectory has increased from a previous measurement time. 6.The computer-implemented method of claim 5, wherein tightening aconstraint on the control input to the BHA comprises determining anincreased value of a weighting factor associated with the control inputto the BHA.
 7. The computer-implemented method of claim 4, wherein thedrilling objective comprises a predicted deviation from the plannedwellbore path, and determining a weighting factor that corresponds to adrilling objective comprises loosening a constraint on the predicteddeviation from the planned wellbore path in a direction in which theuncertainty of the measured wellbore trajectory has increased from aprevious measurement time.
 8. The computer-implemented method of claim7, wherein loosening a constraint on the predicted deviation from theplanned wellbore path comprises determining a reduced value of aweighting factor associated with the predicted deviation from theplanned wellbore path.
 9. The computer-implemented method of claim 3,wherein determining a shape of the wellbore comprises determining aradius of curvature for a subsequent portion of the planned wellborepath.
 10. The computer-implemented method of claim 9, wherein thedrilling objective comprises a predicted deviation from the plannedwellbore path, and determining a weighting factor comprises reducing aconstraint on the predicted deviation from the planned wellbore path ina direction in which the radius of curvature for the subsequent portionof the planned wellbore path has decreased from a previous measurementtime.
 11. The computer-implemented method of claim 3, whereindetermining collision avoidance information comprises determining adirection in which a collision with another wellbore is most likely tooccur.
 12. The computer-implemented method of claim 11, wherein thedrilling objective comprises a predicted deviation from the plannedwellbore path, and determining a weighting factor comprises tightening aconstraint on the predicted deviation from the planned wellbore path inthe direction in which a collision with another wellbore is most likelyto occur.
 13. The computer-implemented method of claim 4, whereindetermining covariance values between a plurality of azimuth values andinclination values for the wellbore trajectory further comprises:determining a plurality of azimuth measurements and inclinationmeasurements received from sensors of the BHA; and determiningcovariance values between the plurality of azimuth measurements andinclination measurements received from the sensors of the BHA.
 14. Thecomputer-implemented method of claim 4, wherein determining covariancevalues between a plurality of azimuth values and inclination values forthe wellbore trajectory further comprises: determining a plurality ofazimuth predictions and inclination predictions based on the model ofBHA dynamics; and determining covariance values between the plurality ofazimuth predictions and inclination predictions based on the model ofBHA dynamics.
 15. The computer-implemented method of claim 1, whereindetermining an objective function comprises: determining a predictedfuture deviation from the planned wellbore path; determining a predictedfuture cost of applying the control input to the BHA; and determining aweighted combination, weighted by the weighting factor, of the predictedfuture deviation from the planned wellbore path and the predicted futurecost of applying the control input to the BHA.
 16. Thecomputer-implemented method of claim 15, wherein determining a weightingfactor comprises: determining a first weighting factor for the predictedfuture deviation from the planned wellbore path; and determining asecond weighting factor for the predicted future cost of applying thecontrol input to the BHA.
 17. The computer-implemented method of claim15, wherein determining a control input to the BHA that satisfies theobjective function comprises determining a control input to the BHA thatminimizes the weighted combination of the predicted future deviationfrom the planned wellbore path and the predicted future cost of applyingthe control input to the BHA over a subsequent period of time.
 18. Thecomputer-implemented method of claim 15, wherein the predicted futurecost of applying the control input to the BHA comprises a predictedenergy consumption for the BHA.
 19. The computer-implemented method ofclaim 1, further comprising: determining a candidate control input tothe BHA; determining a predicted wellbore trajectory, based on thecandidate control input to the BHA and the model of BHA dynamics; anddetermining a predicted future deviation from the planned wellbore pathbased on a deviation between the predicted wellbore trajectory and theplanned wellbore path.
 20. The computer-implemented method of claim 1,wherein determining a control input to the BHA comprises determining atleast one of a first bend angle control, a second bend angle control, afirst packer control, or a second packer control.
 21. Thecomputer-implemented method of claim 1, further comprising: determiningupdated sensor measurements from the BHA; determining an updated modelof BHA dynamics based on the updated sensor measurements from the BHA;determining an updated weighting factor and an updated objectivefunction based on at least one of the updated model of BHA dynamics orthe updated sensor measurements from the BHA; and automatically adaptingthe control input to the BHA that satisfies the updated objectivefunction based on the updated weighting factor.
 22. Thecomputer-implemented method of claim 13, wherein determining covariancevalues between the plurality of azimuth measurements and inclinationmeasurements further comprises determining a cross-correlation betweenuncertainty values in two different directions from the wellboretrajectory.
 23. A system comprising: a first component located at ornear a terranean surface; a bottom hole assembly (BHA) at leastpartially disposed within a wellbore at or near a subterranean zone, theBHA associated with at least one sensor; and a controller communicablycoupled to the first component and the BHA, the controller operable toperform operations comprising: determining sensor measurements from theBHA; determining a model of BHA dynamics based on the sensormeasurements from the BHA; determining a weighting factor thatcorresponds to a drilling objective; determining an objective functioncomprising the drilling objective, weighted by the weighting factor, andone or more constraints; determining a control input to the BHA thatsatisfies the objective function and the one or more constraints; andapplying the control input to the BHA.
 24. A non-transitorycomputer-readable storage medium encoded with at least one computerprogram comprising instructions that, when executed, operate to cause atleast one processor to perform operations for controlling a bottom holeassembly (BHA) to follow a planned wellbore path, the operationscomprising: determining sensor measurements from the BHA; determining amodel of BHA dynamics based on the sensor measurements from the BHA;determining a weighting factor that corresponds to a drilling objective;determining an objective function comprising the drilling objective,weighted by the weighting factor, and one or more constraints;determining a control input to the BHA that satisfies the objectivefunction and the one or more constraints; and applying the control inputto the BHA.